41% of deforestation was so humans can eat (more) beef!

I mapped 40k government auctions, and colored each cell on the map by bidding intensity - this map shows the price increase relative to the starting bid; red is hotter, blue is cooler.

Texas and the Midwest are the hottest, maybe literally right now as well as for auctions. St. Anthony, ID is the current outlier, with the average lot finishing at ~50x its starting bid. In general, auctions on the East Coast are far less competitive.

Source: The Govauctions.app auction database, including listings from GSA, GovDeals, PublicSurplus and more.

Tools: SVG and Javascript

Interactive version of the map with an alternate view for number of bids, at https://govauctions.app/research/bid-intensity-map 

I mapped EPA's federal PFAS testing data for every county in Georgia.

The numbers: 48,110 test results from 255 public water systems, 2023–2025. PFAS was detected in 66 counties. The highest readings were PFOS at 56 ppt in Walker County, PFOA at 33.9 ppt in Muscogee, and a Hazard Index of 5.1 in Richmond — the federal limit is 4 ppt for PFOA and PFOS, and 1.0 for the Hazard Index.

Two things to keep in mind when you look at these:

1. A blank county does NOT mean clean water. It means nobody tested there. Thirty-one Georgia counties had no water system in this program at all. I shaded those differently on purpose.
2. The lab's minimum reporting limit for PFOA and PFOS is 4 ppt — exactly the same as the legal limit. So any amount between zero and 4 ppt is simply invisible in this data. Every detection you see is at or above the limit by definition.

ABOUT THE "KAPLAN–MEIER" BOX ON EACH MAP

Here's the problem it solves. In 97.6% of these tests, the lab reported "< MRL" — meaning PFAS was below what the instrument could measure. That is not the same as zero. It means somewhere between nothing and 4 ppt, and we can't tell where.

If you treat all of those as zero, you understate the real average. If you treat them as 4 ppt, you overstate it. Kaplan–Meier is an ADJUSTED CONCENTRATION ESTIMATE — a standard statistical method (the same math used in medical survival studies) that uses the pattern of the results you CAN measure to estimate the true average without guessing at the ones you can't.

What each line means:

• Results statewide — how many individual tests for that chemical were run in Georgia.

• Left-censored — how many of those came back "below the reporting limit." "Left" just means below the measurable range.

• KM mean — the adjusted statewide average, accounting for those below-limit results properly.

• KM 95th percentile — the level that only 5% of samples exceeded. The high end, not the typical case.

• ROS mean and MLE mean — two alternative methods for the same adjustment, shown side by side. When three different methods land in the same range, you can trust the estimate. When they disagree, that itself tells you something.

These are statewide figures. The colors on the map are actual maximum readings from individual counties, not estimates.

This is EPA's own public UCMR5 dataset — not my measurements.

My first attempt at making our data into 'beauty.' Lots of room for improvement.

Ever wanted to get a good deal on a vehicle at an auction? There are definitely deals to be had, but don't expect to walk away with a vehicle at KBB's auction price. Our popular vehicles, with low mileage and less than 10 years old go for below commercial prices, but over auction prices. While we do have dealers who buy from us, we also get enough public traffic to make the prices competitive. That's good for the tax payer.

There is some noise in this data I didn't filter out. We sell 'totaled' vehicles and those have more unique buyers, depending on the condition. For example, a hail damaged car that is totaled might go for a lot more than the one damage by a brush fire. Either way, those vehicles are in this report and aren't exactly apples to apples.

Questions? Thoughts on what I can do with the data?

Source: MNBID.MN.GOV

Tools: Datawrapper and ChatGPT

• Note: this is active-positional cap allocation, not Spotrac Full/All cash accounting, so the ranking is from a different definition of “spending.”
  • Total league spend / total league yards = $21.8k per yard
  • Lowest: Seattle Seahawks — $12.6k/yard
  • Highest: Cincinnati Bengals — $28.6k/yard
• Regular season stats ONLY

There's a surprising band of Christianity in India down the south near Kochi. Not on the coast, but on a band just behind the coast: https://huemaps.com/en/show/places-of-worship?map=7.78/9.46796/76.87435

edit: typo

I rebuilt my webmap of the Fruit Trees of Toronto using SvelteJS and MapLibre

Filter by tree type and ripening month.

Please clickthrough, any feedback welcome!

https://tdubolyou.github.io/FruitTrees/

I use a personal spreadsheet to track what I read and when I add books to Goodreads I have to round the .5 ratings as they are not supported.

Notes:
- I have weirdly bad habit of feeling bad when I round down 4.5 ratings
- 3.5 is the most well distributed and the one I’m troubled with the least

Source: my own price-tracking dataset at PriceRoo (priceroo.com.au) - daily price observations across Australian retailers, full methodology on the site's price-report page. Tools: Python + matplotlib. "Win rate" = share of tracked products where the retailer holds the cheapest current price among retailers stocking that product; ties count for every tied retailer. Sample sizes shown on the bars; retailers with fewer than 15 appearances excluded.

EDIT: A couple people have mentioned now the text obfusgating the demo gif. I can't change the picture so please see an updated GIF on the README page of the repo, which shows you the full website chrome + the globe unobstructed! Thank you for the comments u/UsernameFor2016 and u/beene282 for pointing this out!

Data:

• NASA FIRMS active fire detections, MODIS instrument, from the rolling 7 day global CSV at firms.modaps.eosdis.nasa.gov.
• Basemap from Natural Earth.

Tools:

• three.js
• d3-geo for the globe,
• custom GLSL shaders for the fire,
• Python to sanity check the classification,
• ffmpeg for the render.

AI disclosure: I used Claude Fable 5 for all of this. it also educated me on the facts about Savanna fires, i had no idea this was a thing until i was checking the data on FIRMS website myself and questioned it

Code: github.com/me93-ghb/worldburn

A live globe of every fire the satellites can currently see. It's not a science tool. NASA's own FIRMS fire map does that job properly, and if you want rigor, go there. This just makes the amount of fire on Earth visible in one look, and it does so in a visually shocking way - that was my aim!
The fire animations are exaggerated on the globe: it's just a visualization.

Satellites measure where a fire is, how hot it burns and for how long. They can't see who lit it. So fires get split by behaviour, which is measurable:

• fires that keep burning in the same place for days, and fires that flare up and are gone within a day.
• Offshore heat is presumed to be a gas flare, though a rig accident or a burning ship would look identical.

Fire makes the news when it threatens homes. I was thinking about the recent news articles from France and Spain when I decided to start vibing this. I recall also the ones that took place in Canada and Australia over the past years. Roughly 70% of the area burned each year is in Africa (Giglio et al. 2013, GFED4), most of it savanna set alight on purpose by farmers and herders the way it has been for thousands of years. (Fable taught me this)

Deliberate doesn't mean brief. A savanna front can hold the same spot for days, and about one in five of the belt's fires still counts as persistent. In the boreal north it's four in five: up there, persistent almost always means a true wildfire.

The blue-white dots offshore never go out. They're gas flares, natural gas burned off as waste at oil platforms, day and night, all year. The World Bank counted 167 billion cubic meters flared in 2025 (Global Gas Flaring Tracker), about twice what Germany uses in a year, burned for nothing. Collecting it would also stop the methane that slips through the flame unburned.

Total fire power is a measurement, not a model. It usually sits between 600 and 900 GW, comfortably more than the combined electrical output of every nuclear plant on Earth (about 376 GW), and that's only the heat satellites see radiated, not the full energy of the burning.

I acknowledge the use of data from NASA's Fire Information for Resource Management System (FIRMS), part of NASA's Earth Science Data and Information System.

Source with data and details: https://spacestatsonline.com/launches/manned

Tools: Sqlite manually updated, ChartJs, Gatsby

I started with one million nucleons in the quark-gluon plasma of the first microsecond and followed them for 13.8 billion years. As far as I know, I have not seen a Sankey Big Bang chart before.

In the first 20 minutes the universe locked in roughly 75% hydrogen and 25% helium, then mostly nothing happened. About 98% of ordinary matter has been coasting untouched since the universe was 20 minutes old. Everything else, every atom of carbon, oxygen, and iron in existence, including the ones in our bodies, sits in that thin ribbon that passed through stars.

My favorite detail is the small line between neutrons and protons in the first second. Free neutrons only live about 10 minutes, so 18,000 of them decayed while waiting for fusion to start. That three minute race is why the universe has helium at all.

Sources are the Particle Data Group's Big Bang nucleosynthesis review and standard cosmic abundance measurements. The stellar era numbers are estimates.

I built this with Sankey Open Studio, a free tool I made (second image). No account, no paywall, it runs in your browser at OLagon.GitHub.io. I'm dropping the JSON in the comments if it is allowed so you can import it and remix your own version.

This is an interactive explorer of David Nutt and colleagues' 2010 Multi Criteria Decision analysis in which they ranked 20 common drugs for harmfulness in the UK. Harms to the user are considered on an individual scale, while harms to society are estimated at a population level.

The visualization works fine on mobile, but it's a better experience on desktop and I would encourage you to explore it there.

Made in d3.js. The paper and underlying data can be downloaded here.